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                  FunRec 推荐系统
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<li class="toctree-l1"><a class="reference internal" href="../chapter_preface/index.html">前言</a></li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_installation/index.html">安装</a></li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_notation/index.html">符号</a></li>
</ul>
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="index.html">1. 推荐系统概述</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="1.intro.html">1.1. 推荐系统是什么？</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">1.2. 本书概览</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_1_retrieval/index.html">2. 召回模型</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_1_retrieval/1.cf/index.html">2.1. 协同过滤</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/1.cf/1.itemcf.html">2.1.1. 基于物品的协同过滤</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/1.cf/2.usercf.html">2.1.2. 基于用户的协同过滤</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/1.cf/3.mf.html">2.1.3. 矩阵分解</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/1.cf/4.summary.html">2.1.4. 总结</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_1_retrieval/2.embedding/index.html">2.2. 向量召回</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/2.embedding/1.i2i.html">2.2.1. I2I召回</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/2.embedding/3.summary.html">2.2.2. 总结</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_1_retrieval/3.sequence/index.html">2.3. 序列召回</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/3.sequence/1.user_interests.html">2.3.1. 深化用户兴趣表示</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/3.sequence/2.generateive_recall.html">2.3.2. 生成式召回方法</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/3.sequence/3.summary.html">2.3.3. 总结</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_2_ranking/index.html">3. 精排模型</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/1.wide_and_deep.html">3.1. 记忆与泛化</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/index.html">3.2. 特征交叉</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/1.second_order.html">3.2.1. 二阶特征交叉</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/2.higher_order.html">3.2.2. 高阶特征交叉</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/3.summary.html">3.2.3. 总结</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/3.sequence.html">3.3. 序列建模</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/index.html">3.4. 多目标建模</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/1.arch.html">3.4.1. 基础结构演进</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/2.dependency_modeling.html">3.4.2. 任务依赖建模</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/3.multi_loss_optim.html">3.4.3. 多目标损失融合</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/4.summary.html">3.4.4. 小结</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/index.html">3.5. 多场景建模</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/1.multi_tower.html">3.5.1. 多塔结构</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/2.dynamic_weight.html">3.5.2. 动态权重建模</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/3.summary.html">3.5.3. 小结</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_3_rerank/index.html">4. 重排模型</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_3_rerank/1.greedy.html">4.1. 基于贪心的重排</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_3_rerank/2.personalized.html">4.2. 基于个性化的重排</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_3_rerank/3.summary.html">4.3. 本章小结</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_4_trends/index.html">5. 难点及热点研究</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_4_trends/1.debias.html">5.1. 模型去偏</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_4_trends/2.cold_start.html">5.2. 冷启动问题</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_4_trends/3.generative.html">5.3. 生成式推荐</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_4_trends/4.summary.html">5.4. 本章小结</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_5_projects/index.html">6. 项目实践</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/1.understanding.html">6.1. 赛题理解</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/2.baseline.html">6.2. Baseline</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/3.analysis.html">6.3. 数据分析</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/4.recall.html">6.4. 多路召回</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/5.feature_engineering.html">6.5. 特征工程</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/6.ranking.html">6.6. 排序模型</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_6_interview/index.html">7. 面试经验</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/1.machine_learning.html">7.1. 机器学习相关</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/2.recommender.html">7.2. 推荐模型相关</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/3.trends.html">7.3. 热门技术相关</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/4.product.html">7.4. 业务场景相关</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/5.hr_other.html">7.5. HR及其他</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_appendix/index.html">8. Appendix</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_appendix/word2vec.html">8.1. Word2vec</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="../chapter_references/references.html">参考文献</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../chapter_preface/index.html">前言</a></li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_installation/index.html">安装</a></li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_notation/index.html">符号</a></li>
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<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="index.html">1. 推荐系统概述</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="1.intro.html">1.1. 推荐系统是什么？</a></li>
<li class="toctree-l2 current"><a class="current reference internal" href="#">1.2. 本书概览</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_1_retrieval/index.html">2. 召回模型</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_1_retrieval/1.cf/index.html">2.1. 协同过滤</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/1.cf/1.itemcf.html">2.1.1. 基于物品的协同过滤</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/1.cf/2.usercf.html">2.1.2. 基于用户的协同过滤</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/1.cf/3.mf.html">2.1.3. 矩阵分解</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/1.cf/4.summary.html">2.1.4. 总结</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_1_retrieval/2.embedding/index.html">2.2. 向量召回</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/2.embedding/1.i2i.html">2.2.1. I2I召回</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/2.embedding/3.summary.html">2.2.2. 总结</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_1_retrieval/3.sequence/index.html">2.3. 序列召回</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/3.sequence/1.user_interests.html">2.3.1. 深化用户兴趣表示</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/3.sequence/2.generateive_recall.html">2.3.2. 生成式召回方法</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_1_retrieval/3.sequence/3.summary.html">2.3.3. 总结</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_2_ranking/index.html">3. 精排模型</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/1.wide_and_deep.html">3.1. 记忆与泛化</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/index.html">3.2. 特征交叉</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/1.second_order.html">3.2.1. 二阶特征交叉</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/2.higher_order.html">3.2.2. 高阶特征交叉</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/3.summary.html">3.2.3. 总结</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/3.sequence.html">3.3. 序列建模</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/index.html">3.4. 多目标建模</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/1.arch.html">3.4.1. 基础结构演进</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/2.dependency_modeling.html">3.4.2. 任务依赖建模</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/3.multi_loss_optim.html">3.4.3. 多目标损失融合</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/4.multi_objective/4.summary.html">3.4.4. 小结</a></li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/index.html">3.5. 多场景建模</a><ul>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/1.multi_tower.html">3.5.1. 多塔结构</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/2.dynamic_weight.html">3.5.2. 动态权重建模</a></li>
<li class="toctree-l3"><a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/3.summary.html">3.5.3. 小结</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_3_rerank/index.html">4. 重排模型</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_3_rerank/1.greedy.html">4.1. 基于贪心的重排</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_3_rerank/2.personalized.html">4.2. 基于个性化的重排</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_3_rerank/3.summary.html">4.3. 本章小结</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_4_trends/index.html">5. 难点及热点研究</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_4_trends/1.debias.html">5.1. 模型去偏</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_4_trends/2.cold_start.html">5.2. 冷启动问题</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_4_trends/3.generative.html">5.3. 生成式推荐</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_4_trends/4.summary.html">5.4. 本章小结</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_5_projects/index.html">6. 项目实践</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/1.understanding.html">6.1. 赛题理解</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/2.baseline.html">6.2. Baseline</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/3.analysis.html">6.3. 数据分析</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/4.recall.html">6.4. 多路召回</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/5.feature_engineering.html">6.5. 特征工程</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_5_projects/6.ranking.html">6.6. 排序模型</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="../chapter_6_interview/index.html">7. 面试经验</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/1.machine_learning.html">7.1. 机器学习相关</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/2.recommender.html">7.2. 推荐模型相关</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/3.trends.html">7.3. 热门技术相关</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/4.product.html">7.4. 业务场景相关</a></li>
<li class="toctree-l2"><a class="reference internal" href="../chapter_6_interview/5.hr_other.html">7.5. HR及其他</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../chapter_appendix/index.html">8. Appendix</a><ul>
<li class="toctree-l2"><a class="reference internal" href="../chapter_appendix/word2vec.html">8.1. Word2vec</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../chapter_references/references.html">参考文献</a></li>
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  <section id="id1">
<h1><span class="section-number">1.2. </span>本书概览<a class="headerlink" href="#id1" title="Permalink to this heading">¶</a></h1>
<p>理解了推荐系统的核心逻辑后，我们现在需要为这个复杂的技术体系绘制一份清晰的地图。本书将按照推荐系统的工业流水线——召回、排序、重排——来组织内容，每个阶段都会深入探讨从经典方法到前沿技术的演进脉络。同时，我们还会关注推荐系统的发展趋势和实际应用，帮你建立从理论到实践的完整知识框架。</p>
<p><strong>第二章：召回系统——从海量到精选的第一道过滤</strong></p>
<p><strong>召回系统</strong>是推荐流水线的起点，需要在毫秒级时间内从亿级候选中筛选出千级相关物品。本章将沿着三条技术演进路径，展现召回技术从简单到复杂、从静态到动态的发展历程。</p>
<figure class="align-default" id="id2">
<span id="chapter-02-outline"></span><a class="reference internal image-reference" href="../_images/chapter_02_outline.svg"><img alt="../_images/chapter_02_outline.svg" src="../_images/chapter_02_outline.svg" width="400px" /></a>
<figcaption>
<p><span class="caption-number">图1.2.1 </span><span class="caption-text">召回模型演化图</span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
<p><strong>协同过滤（Collaborative Filtering）</strong>
是推荐系统的经典起点。我们将首先探讨基于物品的协同过滤（ItemCF，<a class="reference internal" href="../chapter_1_retrieval/1.cf/1.itemcf.html#itemcf"><span class="std std-numref">2.1.1节</span></a>），它通过计算物品间的相似度来推荐与用户历史交互物品相似的其他物品，这是工业界最常用的协同过滤方法。Swing算法（<a class="reference internal" href="../chapter_1_retrieval/1.cf/1.itemcf.html#swing"><span class="std std-numref">2.1.1.2.1节</span></a>）进一步通过分析用户-物品二部图结构来改进相似度计算的鲁棒性，我们将其作为ItemCF的自然延伸介绍。接着介绍基于用户的协同过滤（UserCF，<a class="reference internal" href="../chapter_1_retrieval/1.cf/2.usercf.html#usercf"><span class="std std-numref">2.1.2节</span></a>），它通过寻找相似用户来生成推荐。矩阵分解（Matrix
Factorization，<a class="reference internal" href="../chapter_1_retrieval/1.cf/3.mf.html#matrix-factorization"><span class="std std-numref">2.1.3节</span></a>）技术则标志着协同过滤从邻域方法向向量化表示的范式转变，通过学习用户和物品的隐向量来预测偏好，为后续的向量召回技术奠定了基础。</p>
<p><strong>向量召回（Embedding-based Retrieval）</strong>
技术解决了协同过滤面临的稀疏性和扩展性挑战。通过深度学习方法将用户和物品映射到低维向量空间，推荐问题转化为高效的向量检索问题。我们将详细介绍物品到物品（I2I，<a class="reference internal" href="../chapter_1_retrieval/2.embedding/1.i2i.html#i2i"><span class="std std-numref">2.2.1节</span></a>）召回如何基于用户行为学习物品间的协同关系，以及用户到物品（U2I，<code class="xref std std-numref docutils literal notranslate"><span class="pre">U2I</span></code>）召回如何通过双塔架构直接匹配用户兴趣与候选物品。这些方法不仅大幅提升了召回效率和可扩展性，还能更好地处理冷启动问题。</p>
<p><strong>序列召回（Sequential Retrieval）</strong>
则关注用户行为的时序特性。传统方法将用户历史行为“压扁”成静态向量，丢失了宝贵的时序信息。我们将探讨两种解决思路：一是通过MIND和SDM等模型深化用户兴趣表示（<a class="reference internal" href="../chapter_1_retrieval/3.sequence/1.user_interests.html#user-interests"><span class="std std-numref">2.3.1节</span></a>），捕捉多元兴趣和长短期偏好；二是将推荐视为序列生成任务，通过SASRec、HSTU和TIGER等模型直接预测用户行为序列的下一个物品（<a class="reference internal" href="../chapter_1_retrieval/3.sequence/2.generateive_recall.html#generateive-recall"><span class="std std-numref">2.3.2节</span></a>）。</p>
<p><strong>第三章：排序系统——精准预测用户偏好的核心引擎</strong></p>
<p>经过召回阶段的快速筛选，<strong>排序系统</strong>需要对数千个候选物品进行精准的偏好预测。本章将展现排序模型从简单线性模型到复杂深度学习模型的演进历程。</p>
<figure class="align-default" id="id3">
<span id="chapter-03-outline"></span><a class="reference internal image-reference" href="../_images/chapter_03_outline.svg"><img alt="../_images/chapter_03_outline.svg" src="../_images/chapter_03_outline.svg" width="400px" /></a>
<figcaption>
<p><span class="caption-number">图1.2.2 </span><span class="caption-text">排序模型演化图</span><a class="headerlink" href="#id3" title="Permalink to this image">¶</a></p>
</figcaption>
</figure>
<p>我们从<strong>Wide &amp;
Deep模型</strong>（<a class="reference internal" href="../chapter_2_ranking/1.wide_and_deep.html#wide-and-deep"><span class="std std-numref">3.1节</span></a>）开始，这个模型巧妙地结合了线性模型的“记忆”能力和深度模型的“泛化”能力，为后续模型发展奠定了基础。Wide部分通过人工设计的交叉特征快速学习显性关联规则，Deep部分则通过嵌入层和多层神经网络自动发现深层次的非线性关系，两者通过联合训练实现优势互补。</p>
<p><strong>特征交叉（Feature Crossing）</strong>
是排序模型的核心挑战。用户和物品特征之间的交互往往蕴含着关键的预测信号。我们将系统梳理从人工特征交叉到自动化交叉的技术演进：从FM（Factorization
Machine）的二阶交叉开始（<a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/1.second_order.html#second-order-feature-crossing"><span class="std std-numref">3.2.1节</span></a>），它通过隐向量内积高效建模特征间的两两交互；到DeepFM、xDeepFM等模型实现的高阶特征交叉（<a class="reference internal" href="../chapter_2_ranking/2.feature_crossing/2.higher_order.html#higher-order-feature-crossing"><span class="std std-numref">3.2.2节</span></a>），它们通过深度网络自动学习复杂的特征组合；再到AutoInt等基于自注意力机制的模型，实现更加灵活和个性化的特征交互建模。</p>
<p><strong>序列建模</strong>（<a class="reference internal" href="../chapter_2_ranking/3.sequence.html#sequence-modeling"><span class="std std-numref">3.3节</span></a>）在排序阶段同样重要。用户的行为序列蕴含着丰富的时序信息和动态兴趣演化模式。我们将沿着序列建模技术的演进路径介绍三个代表性模型：首先是DIN（<code class="xref std std-numref docutils literal notranslate"><span class="pre">din</span></code>），它通过自适应注意力机制解决传统方法中用户兴趣表示固定的问题，能够根据候选物品动态调整用户历史行为的权重；在此基础上，DIEN（<code class="xref std std-numref docutils literal notranslate"><span class="pre">dien</span></code>）进一步引入时序建模，通过兴趣提取层和兴趣演化层显式建模用户兴趣的动态变化过程；而DSIN（<code class="xref std std-numref docutils literal notranslate"><span class="pre">dsin</span></code>）则从会话视角重新审视用户行为，通过分层建模捕捉会话内的集中意图和会话间的兴趣迁移，为序列建模提供了新的建模范式。</p>
<p><strong>多目标优化（Multi-Objective Optimization）</strong>
解决了单一指标优化的局限性。真实的推荐场景往往需要同时优化点击率、转化率、消费时长等多个指标。我们将探讨多目标建模的核心挑战，包括任务相关性冲突、跷跷板效应等问题，以及通过模型架构设计（<a class="reference internal" href="../chapter_2_ranking/4.multi_objective/1.arch.html#multi-objective-arch"><span class="std std-numref">3.4.1节</span></a>）,
任务依赖建模（<a class="reference internal" href="../chapter_2_ranking/4.multi_objective/2.dependency_modeling.html#dependency-modeling"><span class="std std-numref">3.4.2节</span></a>）和损失函数优化（<a class="reference internal" href="../chapter_2_ranking/4.multi_objective/3.multi_loss_optim.html#multi-loss-optim"><span class="std std-numref">3.4.3节</span></a>）来实现多目标的协同优化。</p>
<p><strong>多场景建模（Multi-Scenario Modeling）</strong>
则关注同一平台下不同场景（如首页推荐、详情页推荐、搜索推荐）的差异化建模。我们将介绍多塔架构（<a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/1.multi_tower.html#multi-tower"><span class="std std-numref">3.5.1节</span></a>）、动态权重（<a class="reference internal" href="../chapter_2_ranking/5.multi_scenario/2.dynamic_weight.html#dynamic-weight"><span class="std std-numref">3.5.2节</span></a>）等技术如何实现场景间的知识共享与个性化适配。</p>
<p><strong>第四章：重排系统——优化用户体验的最后一环</strong></p>
<p><strong>重排系统</strong>是推荐流水线的最后阶段，其目标不再是预测单个物品的分数，而是对整个推荐列表进行全局优化。本章将探讨如何解决多样性、新颖性、公平性等复杂的用户体验问题。</p>
<p><strong>贪心重排策略</strong> （<a class="reference internal" href="../chapter_3_rerank/1.greedy.html#greedy-rerank"><span class="std std-numref">4.1节</span></a>）
是重排算法的经典方法。我们将介绍MMR（Maximal Marginal
Relevance）如何在相关性和多样性之间取得平衡，以及DPP（Determinantal
Point Process）如何通过优雅的数学框架实现多样性约束下的最优化。</p>
<p><strong>个性化重排策略</strong> （<a class="reference internal" href="../chapter_3_rerank/2.personalized.html#personalized-rerank"><span class="std std-numref">4.2节</span></a>）
则考虑不同用户对多样性的不同需求。PRM（Personalized Re-ranking
Model）通过Transformer架构融合用户偏好与物品交互，实现端到端的个性化重排；PRS（Personalized
Re-ranking
Strategy）进一步考虑排列组合的影响，通过两阶段优化捕捉排列变异效应。</p>
<p><strong>第五章：前沿趋势——推荐系统的未来方向</strong></p>
<p>推荐系统作为一个活跃的研究领域，不断涌现新的挑战和解决方案。本章将关注几个重要的发展趋势。</p>
<p><strong>偏差消除（Debiasing）</strong> (<a class="reference internal" href="../chapter_4_trends/1.debias.html#debias"><span class="std std-numref">5.1节</span></a>)
解决推荐系统中的各种偏差问题。推荐系统的训练数据来源于用户真实交互，不可避免地包含选择偏差、曝光偏差、位置偏差等多种偏差，这些偏差会通过反馈闭环不断放大。我们将介绍两种经典纠偏方法：逆倾向得分（IPS）通过重新加权消除选择偏差，位置感知学习（PAL）通过双模块架构分离位置影响与用户真实偏好。</p>
<p><strong>冷启动问题（Cold Start Problem）</strong> (<a class="reference internal" href="../chapter_4_trends/2.cold_start.html#cold-start"><span class="std std-numref">5.2节</span></a>)
始终是推荐系统面临的核心挑战，我们将探讨如何为新用户和新物品提供有效推荐。</p>
<p><strong>生成式推荐（Generative Recommendation）</strong>
(<a class="reference internal" href="../chapter_4_trends/3.generative.html#generative-recommendation"><span class="std std-numref">5.3节</span></a>)
代表了推荐系统的新范式，通过大语言模型等生成式AI技术，推荐系统正在从传统的“检索+排序”模式向“理解+生成”模式转变。</p>
<p><strong>第六章：项目实践——构建完整推荐系统</strong></p>
<p>理论学习的最终目标是实际应用。本章 (<a class="reference internal" href="../chapter_5_projects/index.html#chap-projects"><span class="std std-numref">6节</span></a>)
将通过完整的项目案例，展示如何将前面章节的算法和技术整合成一个可运行的推荐系统。</p>
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